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Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma
- Source :
- Evolutionary Bioinformatics, Evolutionary Bioinformatics, Vol 16 (2020)
- Publication Year :
- 2020
- Publisher :
- SAGE Publications, 2020.
-
Abstract
- This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to “plasma membrane structure,” “sensory perception,” “metabolism,” and “cell proliferation.” Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.
- Subjects :
- Hepatocellular carcinoma
lcsh:Evolution
Computational biology
Biology
Transcriptome
03 medical and health sciences
0302 clinical medicine
microRNA
Genetics
medicine
lcsh:QH359-425
KEGG
Gene
Ecology, Evolution, Behavior and Systematics
Survival analysis
030304 developmental biology
Original Research
0303 health sciences
Cell growth
WGCNA
medicine.disease
Computer Science Applications
differential expression analysis
030220 oncology & carcinogenesis
Biomarker (medicine)
biomarker
prognosis
Subjects
Details
- Language :
- English
- ISSN :
- 11769343
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- Evolutionary Bioinformatics Online
- Accession number :
- edsair.doi.dedup.....3751cf64ac989f56bb737b1567e7f9cf